feat: added example of usage

llama-index-packs/llama-index-packs-ersatz-o1/README.md

@@ -1 +1,10 @@
-# LlamaIndex Packs Integration: Ersatz_O1
+# ErsatzO1 Query Engine.
+
+This module implements the ErsatzO1 approach, combining Chain of Thought (CoT) with
+re-reading technique and SELF-CONSISTENCY prompting for generic text input.
+
+The approach is adapted from the concepts presented in various papers on prompting techniques.
+
+## Sources
+- [Re-Reading Improves Reasoning in Large Language Models](https://arxiv.org/pdf/2309.06275)
+- [SELF-CONSISTENCY IMPROVES CHAIN OF THOUGHT REASONING IN LANGUAGE MODELS](https://arxiv.org/pdf/2203.11171)

llama-index-packs/llama-index-packs-ersatz-o1/examples/simple_planing.py

@@ -0,0 +1,26 @@
+import asyncio
+
+from llama_index.core.llms import LLM
+from llama_index.llms.openai import OpenAI
+
+from llama_index.packs.ersatz_o1 import ErsatzO1QueryEngine
+
+llm = OpenAI(model="gpt-4-turbo")
+
+task_context = """
+Every day, Wendi feeds each of her chickens three cups of mixed chicken feed, containing seeds, mealworms and vegetables
+to help keep them healthy. She gives the chickens their feed in three separate meals. In the morning, she gives her flock of
+chickens 15 cups of feed. In the afternoon, she gives her chickens another 25 cups of feed.  
+"""
+
+query_engine = ErsatzO1QueryEngine(
+    context=task_context,
+    llm=llm,
+    reasoning_paths=5,
+    verbose=True,
+)
+
+if __name__ == "__main__":
+    res = query_engine.query("How many cups of feed does she need to give her chickens in the final meal of the day if the size of Wendi’s flock is 20 chickens?")
+    print(res)
+

llama-index-packs/llama-index-packs-ersatz-o1/llama_index/packs/ersatz_o1/base.py

@@ -39,17 +39,22 @@
 Final Answer: Provide your answer here. Ensure it is as concise as possible, without any explanation.
 
 Confidence: Rate your confidence in the answer from 1 (low) to 5 (high).
+
+Your response should be in the exact format:
+
+<answer>
+<confidence_value>
+
+Replace <answer> with your actual answer and <confidence_value> with your actual confidence rating.
 """
 text_prompt = PromptTemplate(template=text_prompt_str)
 
-
 class FinalAnswerOutputParser(ChainableOutputParser):
     """Output parser for the ErsatzO1 approach."""
-
     def parse(self, output: str) -> Optional[str]:
-        lines = output.split("Final Answer:")
-        if len(lines) > 1:
-            return lines[-1].strip()
+        lines = output.strip().split('\n')
+        if len(lines) >= 2:
+            return output.strip()  # Return the entire formatted output
         return None
 
     def format(self, query: Any) -> str:
@@ -58,8 +63,13 @@ def format(self, query: Any) -> str:
 
 def parse_response(response: str) -> Tuple[str, int]:
     """Parse the response from the LLM in the ErsatzO1 approach."""
-    answer = response.split("Final Answer:")[1].split("Confidence:")[0].strip()
-    confidence = int(response.split("Confidence:")[1].strip())
+    lines = response.strip().split('\n')
+    if len(lines) != 2:
+        raise ValueError("Response format is incorrect")
+
+    answer = lines[0].strip("'")  # Remove surrounding quotes if present
+    confidence = int(lines[1].strip())
+
     return answer, confidence
 
 
@@ -69,7 +79,7 @@ async def async_textual_reasoning(
         llm: LLM,
         num_paths: int = 5,
         verbose: bool = False,
-        temperature: float = 0.7,
+        temperature: float = 0.01,
 ) -> List[Tuple[str, int]]:
     """
     Perform asynchronous textual reasoning using the ErsatzO1 approach.